Spectral Camera based on Ghost Imaging via Sparsity Constraints

TL;DR

This paper introduces a GISC spectral camera that leverages ghost imaging and compressive sensing to acquire spectral images efficiently, approaching the Shannon Limit and enabling high-resolution 3D spectral data capture with a single exposure.

Contribution

It presents the first experimental demonstration of a spectral camera using ghost imaging with sparsity constraints, significantly improving information acquisition efficiency.

Findings

Achieves spectral image acquisition below the Nyquist rate

Enables high-resolution 3D spectral imaging with a 2D detector

Approaches the Shannon Limit in optical imaging

Abstract

The image information acquisition ability of a conventional camera is usually much lower than the Shannon Limit since it does not make use of the correlation between pixels of image data. Applying a random phase modulator to code the spectral images and combining with compressive sensing (CS) theory, a spectral camera based on true thermal light ghost imaging via sparsity constraints (GISC spectral camera) is proposed and demonstrated experimentally. GISC spectral camera can acquire the information at a rate significantly below the Nyquist rate, and the resolution of the cells in the three-dimensional (3D) spectral images data-cube can be achieved with a two-dimensional (2D) detector in a single exposure. For the first time, GISC spectral camera opens the way of approaching the Shannon Limit determined by Information Theory in optical imaging instruments.